Hyper-accuracy in local weather forecasting

Local Weather Data x Critical Risk Management We talk a lot about environmental impacts on high-risk activities—like wind speed & direction impacting crane lifts, work at height, and heavy equipment operations—but how representative is the weather data we rely on? Most of the time, we use forecasted conditions from national meteorological services which are great for general awareness but often don’t reflect site-specific conditions. A forecast from a weather station 30km away doesn’t capture sudden wind gusts at a crane lift zone, temperature variations on-site, or microclimates created by terrain. Having local, real-time weather data at the actual worksite enables better risk management decisions. Instead of relying on broad forecasts, organisations can monitor live conditions at the precise location where critical work is happening. PLUS you get your own comprehensive data set for analytics... In the photos I'm holding a Davis EnviroMonitor Gateway LTE & Vantage Pro2 GroWeather Sensor Suite which is an example of a local weather monitoring system. This system provides real-time, hyper-local weather data directly from the worksite, enabling data-driven risk management decisions. It delivers real-time updates every 2.5 seconds; has wind speed, temperature, humidity, and rainfall monitoring plus solar radiation and evapotranspiration data which is also valuable for heat stress risk. This model has LTE connectivity (basically you can stick a SIM card in it) for remote monitoring and integration with cloud platforms. These systems aren't that expensive and offer new insights for local risk management that I've found can make a pretty big difference to your risk control strategy. Is anyone else implementing local weather systems for crane ops or other critical risk management? #safetytech #safetyinnovation #IoT

Every year, natural disasters hit harder and closer to home. But when city leaders ask, "How will rising heat or wildfire smoke impact my home in 5 years?"—our answers are often vague. Traditional climate models give sweeping predictions, but they fall short at the local level. It's like trying to navigate rush hour using a globe instead of a street map. That’s where generative AI comes in. This year, our team at Google Research built a new genAI method to project climate impacts—taking predictions from the size of a small state to the size of a small city. Our approach provides: - Unprecedented detail – in regional environmental risk assessments at a small fraction of the cost of existing techniques - Higher accuracy – reduced fine-scale errors by over 40% for critical weather variables and reduces error in extreme heat and precipitation projections by over 20% and 10% respectively - Better estimates of complex risks – Demonstrates remarkable skill in capturing complex environmental risks due to regional phenomena, such as wildfire risk from Santa Ana winds, which statistical methods often miss Dynamical-generative downscaling process works in two steps: 1) Physics-based first pass: First, a regional climate model downscales global Earth system data to an intermediate resolution (e.g., 50 km) – much cheaper computationally than going straight to very high resolution. 2) AI adds the fine details: Our AI-based Regional Residual Diffusion-based Downscaling model (“R2D2”) adds realistic, fine-scale details to bring it up to the target high resolution (typically less than 10 km), based on its training on high-resolution weather data. Why does this matter? Governments and utilities need these hyperlocal forecasts to prepare emergency response, invest in infrastructure, and protect vulnerable neighborhoods. And this is just one way AI is turbocharging climate resilience. Our teams at Google are already using AI to forecast floods, detect wildfires in real time, and help the UN respond faster after disasters. The next chapter of climate action means giving every city the tools to see—and shape—their own future. Congratulations Ignacio Lopez Gomez, Tyler Russell MBA, PMP, and teams on this important work! Discover the full details of this breakthrough: https://lnkd.in/g5u_WctW  PNAS Paper: https://lnkd.in/gr7Acz25

Good morning, Meteorologists and Atmospheric Scientists around the globe! Today, let's discuss why regional numerical weather models, such as the Weather Research and Forecasting (#WRF) model developed by NSF NCAR - The National Center for Atmospheric Research, are incredibly valuable to meteorologists worldwide. While global weather models like NOAA: National Oceanic & Atmospheric Administration's Global Forecast System (#GFS) and the European Centre for Medium-Range Weather Forecasts - ECMWF) model are widely utilized, they typically have grid spacing (resolution) of roughly 27km and 9km, respectively. Despite #ECMWF's finer resolution, it still falls within the convective "grey zone." This zone describes a modeling challenge where resolutions are too coarse to explicitly resolve convection yet too fine for convection to be adequately parameterized. Regional models like WRF address this limitation by taking the coarse resolution data from global models and downscaling it to finer grid spacing. This process significantly enhances forecast quality, providing more detailed and accurate representations of meteorological features. For instance, I recently conducted a WRF simulation over Côte d'Ivoire, driven by GFS data, using grids of 20km (to represent the approximate GFS native resolution) and 4km (convective-resolving scale). I've attached images highlighting the notable differences between these resolutions. First, consider the representation of topography. Due to its coarse grid spacing, the global model's resolution smooths out critical features such as river valleys, coastal inlets, and smaller #orographic details, potentially degrading forecast accuracy. In contrast, the high-resolution 4km WRF simulation clearly depicts these detailed terrain features. Second, let's examine precipitation forecasts. Both model resolutions can simulate precipitation totals effectively; however, the spatial distribution significantly differs. For example, the coarser 20km grid indicates an entire region near Dimbokro receiving uniform precipitation (e.g., 50-100mm). Meanwhile, the finer-scale 4km WRF model reveals a more nuanced and accurate distribution of rainfall across smaller areas, greatly improving the precision of forecasts. In summary, regional numerical models like WRF provide meteorologists with significantly enhanced spatial resolution, allowing for more detailed, accurate forecasts and better-informed weather predictions. These capabilities are essential for effective decision-making, particularly in areas sensitive to precise weather conditions. #Meteorology #WRF #WeatherModels #NumericalWeatherPrediction #AtmosphericScience #Forecasting #GFS #ECMWF #ConvectiveGreyZone #WeatherResearch